Here are 5 Innovative Uses for Machine Learning

Though its time horizon can’t be predicted, artificial intelligence (AI) promises to foundationally influence modern society, for better or worse. A sub-genre of AI — machine learning — has garnered particular attention from the pundits for its potential impact on the world’s most important industries.

Due to the resulting hype, massive amounts of talent and resources are entering this space.

But what is machine learning and why should we care about it in the first place? The answer is that, in the broadest sense, machine learning models are an application of AI in which algorithms independently predict outcomes. In other words, these models can process large data sets, extract insights and make accurate predictions without the need for much human intervention.

Numerous value-generating implications result from the accelerated development of this technology, and many are poised to radically streamline the business world. Here are five of the most innovative use cases for machine learning. They’ll be coming into your life — at least your business life — sooner than you think.

1. Widescale use of autonomous vehicles

The wide-scale adoption of autonomous vehicles represents a far more efficient future for transportation. Early reports indicate that self-driving cars could reduce traffic-related fatalities by as much as 90 percent.

Though we’re probably a few years away from consumer production, the adoption of autonomous vehicles by society is, at this point, inevitable. However, the time scale for adoption of this technology largely depends upon regulatory action, which often lies outside of the tech world’s control.

Software engineers developing these self-driving “fleets of the future” are relying heavily upon machine-learning technologies to power the algorithms that enable vehicles to operate autonomously. These models effectively integrate data points from a number of different sensors — lidar (a survey method using lasers), radars and cameras — to operate the vehicle. These deep-learning algorithms become more intelligent over time, leading to safer driving.

2. A more efficient healthcare network

Although a critical part of the economy, the healthcare industry still operates on top of an inefficient legacy infrastructure. A major point of concern is finding ways to preserve sensitive patient details while still optimizing the system.

Luckily, we can apply innovative machine learning algorithms (that operate without humans) to process large sets of healthcare data without breaching confidentiality contracts. Furthermore, we can use these models to better analyze and understand diagnoses, risk factors and coefficients of causation.

As Dr. Ed Corbett has pointed out: “It’s clear that machine learning puts another arrow in the quiver of clinical decision making.